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AI-200 Containerized AI Workloads Practice Question

You are managing an AKS cluster running resource-intensive AI training jobs. You want to ensure that nodes automatically scale up and down based on pending pod resource requests. Which component should you enable and configure?

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

Kubernetes Cluster Autoscaler

The Kubernetes Cluster Autoscaler monitors for unschedulable pods and requests additional nodes from Azure Virtual Machine Scale Sets.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • KEDA (Kubernetes Event-driven Autoscaling)

    Why it's wrong here

    KEDA scales pods based on event sources like queues, not cluster node infrastructure.

  • Azure Load Balancer

    Why it's wrong here

    Azure Load Balancer routes network traffic, it does not manage compute node provisioning.

  • Horizontal Pod Autoscaler

    Why it's wrong here

    Horizontal Pod Autoscaler scales the number of pod replicas, not the underlying physical or virtual cluster nodes.

  • Kubernetes Cluster Autoscaler

    Why this is correct

    Cluster Autoscaler automatically adjusts the number of nodes in your AKS cluster based on resource demands.

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JA

Written and reviewed by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

Last reviewed August 2026 · checked against the official Microsoft exam blueprint

This AI-200 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-200 exam.